Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations1.000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.9 KiB
Average record size in memory96.1 B

Variable types

Categorical6
Numeric6

Alerts

address is highly overall correlated with age and 1 other fieldsHigh correlation
age is highly overall correlated with address and 2 other fieldsHigh correlation
employ is highly overall correlated with age and 2 other fieldsHigh correlation
income is highly overall correlated with employHigh correlation
marital is highly overall correlated with resideHigh correlation
reside is highly overall correlated with maritalHigh correlation
retire is highly overall correlated with ageHigh correlation
tenure is highly overall correlated with address and 1 other fieldsHigh correlation
retire is highly imbalanced (72.6%) Imbalance
address has 56 (5.6%) zeros Zeros
employ has 106 (10.6%) zeros Zeros

Reproduction

Analysis started2024-11-27 13:18:55.040665
Analysis finished2024-11-27 13:18:58.543282
Duration3.5 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

region
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
344 
2
334 
1
322 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1.000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 344
34.4%
2 334
33.4%
1 322
32.2%

Length

2024-11-27T14:18:58.626099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T14:18:58.692556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 344
34.4%
2 334
33.4%
1 322
32.2%

Most occurring characters

ValueCountFrequency (%)
3 344
34.4%
2 334
33.4%
1 322
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 344
34.4%
2 334
33.4%
1 322
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 344
34.4%
2 334
33.4%
1 322
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 344
34.4%
2 334
33.4%
1 322
32.2%

tenure
Real number (ℝ)

High correlation 

Distinct72
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.526
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T14:18:58.777046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median34
Q354
95-th percentile70
Maximum72
Range71
Interquartile range (IQR)37

Descriptive statistics

Standard deviation21.359812
Coefficient of variation (CV)0.60124449
Kurtosis-1.228352
Mean35.526
Median Absolute Deviation (MAD)18
Skewness0.11185995
Sum35526
Variance456.24157
MonotonicityNot monotonic
2024-11-27T14:18:58.892670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 31
 
3.1%
24 20
 
2.0%
48 20
 
2.0%
3 20
 
2.0%
13 19
 
1.9%
4 19
 
1.9%
20 19
 
1.9%
5 19
 
1.9%
22 19
 
1.9%
16 19
 
1.9%
Other values (62) 795
79.5%
ValueCountFrequency (%)
1 13
1.3%
2 7
 
0.7%
3 20
2.0%
4 19
1.9%
5 19
1.9%
6 15
1.5%
7 18
1.8%
8 14
1.4%
9 15
1.5%
10 18
1.8%
ValueCountFrequency (%)
72 31
3.1%
71 17
1.7%
70 11
 
1.1%
69 14
1.4%
68 9
 
0.9%
67 16
1.6%
66 8
 
0.8%
65 17
1.7%
64 11
 
1.1%
63 6
 
0.6%

age
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.684
Minimum18
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T14:18:58.974719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23
Q132
median40
Q351
95-th percentile64
Maximum77
Range59
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.558816
Coefficient of variation (CV)0.30128626
Kurtosis-0.60416361
Mean41.684
Median Absolute Deviation (MAD)9
Skewness0.35666365
Sum41684
Variance157.72387
MonotonicityNot monotonic
2024-11-27T14:18:59.042867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 39
 
3.9%
39 35
 
3.5%
35 34
 
3.4%
31 32
 
3.2%
34 32
 
3.2%
37 31
 
3.1%
40 28
 
2.8%
42 28
 
2.8%
30 26
 
2.6%
52 25
 
2.5%
Other values (50) 690
69.0%
ValueCountFrequency (%)
18 1
 
0.1%
19 4
 
0.4%
20 10
1.0%
21 8
 
0.8%
22 15
1.5%
23 16
1.6%
24 20
2.0%
25 23
2.3%
26 21
2.1%
27 24
2.4%
ValueCountFrequency (%)
77 1
 
0.1%
76 3
0.3%
75 2
 
0.2%
74 1
 
0.1%
73 1
 
0.1%
72 1
 
0.1%
71 1
 
0.1%
70 3
0.3%
69 6
0.6%
68 6
0.6%

marital
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
505 
1
495 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1.000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 505
50.5%
1 495
49.5%

Length

2024-11-27T14:18:59.125819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T14:18:59.175752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 505
50.5%
1 495
49.5%

Most occurring characters

ValueCountFrequency (%)
0 505
50.5%
1 495
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 505
50.5%
1 495
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 505
50.5%
1 495
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 505
50.5%
1 495
49.5%

address
Real number (ℝ)

High correlation  Zeros 

Distinct50
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.551
Minimum0
Maximum55
Zeros56
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T14:18:59.266586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q318
95-th percentile31
Maximum55
Range55
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.086681
Coefficient of variation (CV)0.87323014
Kurtosis0.85972949
Mean11.551
Median Absolute Deviation (MAD)6
Skewness1.106246
Sum11551
Variance101.74114
MonotonicityNot monotonic
2024-11-27T14:18:59.343019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 68
 
6.8%
2 66
 
6.6%
4 61
 
6.1%
3 61
 
6.1%
0 56
 
5.6%
7 53
 
5.3%
5 50
 
5.0%
9 41
 
4.1%
8 39
 
3.9%
10 38
 
3.8%
Other values (40) 467
46.7%
ValueCountFrequency (%)
0 56
5.6%
1 68
6.8%
2 66
6.6%
3 61
6.1%
4 61
6.1%
5 50
5.0%
6 36
3.6%
7 53
5.3%
8 39
3.9%
9 41
4.1%
ValueCountFrequency (%)
55 1
 
0.1%
49 1
 
0.1%
48 1
 
0.1%
46 1
 
0.1%
45 1
 
0.1%
44 3
0.3%
43 3
0.3%
42 1
 
0.1%
41 1
 
0.1%
40 3
0.3%

income
Real number (ℝ)

High correlation 

Distinct218
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.535
Minimum9
Maximum1668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T14:18:59.425733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile18
Q129
median47
Q383
95-th percentile232.25
Maximum1668
Range1659
Interquartile range (IQR)54

Descriptive statistics

Standard deviation107.04416
Coefficient of variation (CV)1.3805915
Kurtosis69.697327
Mean77.535
Median Absolute Deviation (MAD)21.5
Skewness6.6432718
Sum77535
Variance11458.453
MonotonicityNot monotonic
2024-11-27T14:18:59.527592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 24
 
2.4%
26 22
 
2.2%
41 22
 
2.2%
33 20
 
2.0%
46 19
 
1.9%
22 18
 
1.8%
31 18
 
1.8%
21 18
 
1.8%
34 17
 
1.7%
28 17
 
1.7%
Other values (208) 805
80.5%
ValueCountFrequency (%)
9 7
0.7%
10 3
 
0.3%
11 2
 
0.2%
12 3
 
0.3%
13 2
 
0.2%
14 7
0.7%
15 8
0.8%
16 4
 
0.4%
17 13
1.3%
18 15
1.5%
ValueCountFrequency (%)
1668 1
0.1%
1131 1
0.1%
944 1
0.1%
928 1
0.1%
732 1
0.1%
674 1
0.1%
608 1
0.1%
591 1
0.1%
587 1
0.1%
508 1
0.1%

ed
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
287 
4
234 
3
209 
1
204 
5
66 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1.000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 287
28.7%
4 234
23.4%
3 209
20.9%
1 204
20.4%
5 66
 
6.6%

Length

2024-11-27T14:18:59.616339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T14:18:59.676606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 287
28.7%
4 234
23.4%
3 209
20.9%
1 204
20.4%
5 66
 
6.6%

Most occurring characters

ValueCountFrequency (%)
2 287
28.7%
4 234
23.4%
3 209
20.9%
1 204
20.4%
5 66
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 287
28.7%
4 234
23.4%
3 209
20.9%
1 204
20.4%
5 66
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 287
28.7%
4 234
23.4%
3 209
20.9%
1 204
20.4%
5 66
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 287
28.7%
4 234
23.4%
3 209
20.9%
1 204
20.4%
5 66
 
6.6%

employ
Real number (ℝ)

High correlation  Zeros 

Distinct46
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.987
Minimum0
Maximum47
Zeros106
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T14:18:59.754644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q317
95-th percentile31
Maximum47
Range47
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.082087
Coefficient of variation (CV)0.91763785
Kurtosis0.53089069
Mean10.987
Median Absolute Deviation (MAD)6
Skewness1.0610487
Sum10987
Variance101.64848
MonotonicityNot monotonic
2024-11-27T14:18:59.825912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 106
 
10.6%
1 66
 
6.6%
2 59
 
5.9%
5 54
 
5.4%
4 52
 
5.2%
3 50
 
5.0%
7 48
 
4.8%
6 44
 
4.4%
9 39
 
3.9%
8 38
 
3.8%
Other values (36) 444
44.4%
ValueCountFrequency (%)
0 106
10.6%
1 66
6.6%
2 59
5.9%
3 50
5.0%
4 52
5.2%
5 54
5.4%
6 44
4.4%
7 48
4.8%
8 38
 
3.8%
9 39
 
3.9%
ValueCountFrequency (%)
47 1
 
0.1%
45 2
 
0.2%
44 2
 
0.2%
43 3
0.3%
41 2
 
0.2%
40 3
0.3%
39 3
0.3%
38 1
 
0.1%
37 5
0.5%
36 4
0.4%

retire
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0.0
953 
1.0
 
47

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3.000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 953
95.3%
1.0 47
 
4.7%

Length

2024-11-27T14:18:59.909646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T14:18:59.976249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 953
95.3%
1.0 47
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 1953
65.1%
. 1000
33.3%
1 47
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1953
65.1%
. 1000
33.3%
1 47
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1953
65.1%
. 1000
33.3%
1 47
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1953
65.1%
. 1000
33.3%
1 47
 
1.6%

gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
517 
0
483 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1.000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

Length

2024-11-27T14:19:00.035376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T14:19:00.093153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

Most occurring characters

ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 517
51.7%
0 483
48.3%

reside
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.331
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-11-27T14:19:00.142609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4357926
Coefficient of variation (CV)0.61595566
Kurtosis0.4044744
Mean2.331
Median Absolute Deviation (MAD)1
Skewness1.0334458
Sum2331
Variance2.0615005
MonotonicityNot monotonic
2024-11-27T14:19:00.225782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 375
37.5%
2 272
27.2%
3 138
 
13.8%
4 120
 
12.0%
5 60
 
6.0%
6 29
 
2.9%
7 4
 
0.4%
8 2
 
0.2%
ValueCountFrequency (%)
1 375
37.5%
2 272
27.2%
3 138
 
13.8%
4 120
 
12.0%
5 60
 
6.0%
6 29
 
2.9%
7 4
 
0.4%
8 2
 
0.2%
ValueCountFrequency (%)
8 2
 
0.2%
7 4
 
0.4%
6 29
 
2.9%
5 60
 
6.0%
4 120
 
12.0%
3 138
 
13.8%
2 272
27.2%
1 375
37.5%

custcat
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
281 
1
266 
4
236 
2
217 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1.000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 281
28.1%
1 266
26.6%
4 236
23.6%
2 217
21.7%

Length

2024-11-27T14:19:00.293279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T14:19:00.359446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 281
28.1%
1 266
26.6%
4 236
23.6%
2 217
21.7%

Most occurring characters

ValueCountFrequency (%)
3 281
28.1%
1 266
26.6%
4 236
23.6%
2 217
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 281
28.1%
1 266
26.6%
4 236
23.6%
2 217
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 281
28.1%
1 266
26.6%
4 236
23.6%
2 217
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 281
28.1%
1 266
26.6%
4 236
23.6%
2 217
21.7%

Interactions

2024-11-27T14:18:57.724841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:55.558095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.008891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.448168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.868751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.324472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.795101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:55.672053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.071790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.526715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.958070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.402375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.870335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:55.721936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.124991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.592281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.040034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.464083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.959232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:55.791323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.189094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.675011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.102535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.524462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:58.042763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:55.869884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.252030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.739498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.196710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.591319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:58.126699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:55.933060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.338385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:56.805849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.256937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-27T14:18:57.654222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-27T14:19:00.432700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
addressagecustcatedemploygenderincomemaritalregionresideretiretenure
address1.0000.6370.0800.0660.4350.0000.2990.0000.000-0.1860.3820.503
age0.6371.0000.0680.1000.6640.1040.4390.0760.000-0.2340.6410.490
custcat0.0800.0681.0000.2230.1140.0000.0560.0860.0000.0520.0770.193
ed0.0660.1000.2231.0000.1020.0260.0000.0000.0800.0000.1090.049
employ0.4350.6640.1140.1021.0000.0980.6370.0560.000-0.1210.3410.513
gender0.0000.1040.0000.0260.0981.0000.0000.0000.0240.0000.0380.000
income0.2990.4390.0560.0000.6370.0001.0000.0140.000-0.0900.0000.341
marital0.0000.0760.0860.0000.0560.0000.0141.0000.0810.7740.0660.180
region0.0000.0000.0000.0800.0000.0240.0000.0811.0000.0000.0000.000
reside-0.186-0.2340.0520.000-0.1210.000-0.0900.7740.0001.0000.125-0.001
retire0.3820.6410.0770.1090.3410.0380.0000.0660.0000.1251.0000.193
tenure0.5030.4900.1930.0490.5130.0000.3410.1800.000-0.0010.1931.000

Missing values

2024-11-27T14:18:58.342522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-27T14:18:58.457418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

regiontenureagemaritaladdressincomeedemployretiregenderresidecustcat
0213441964.0450.0021
13113317136.0550.0064
236852124116.01290.0123
32333301233.0200.0111
4223301930.0120.0043
52413901778.02160.0113
6345221219.0240.0152
7238350576.02100.0034
83455917166.04310.0053
91684112172.01220.0032
regiontenureagemaritaladdressincomeedemployretiregenderresidecustcat
990150430627.0340.0013
99126150116190.02220.0122
9921345212106.02190.0023
9933265413026.0310.0124
9941154611763.0510.0024
995310390027.0300.0131
99617340222.0550.0111
99736759040944.05330.0114
9983704901887.02220.0113
999350361739.0330.0132